Environmental effects on brain functional networks in a juvenile twin population

The brain’s intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10–30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.


Fig. S1. Schematic illustration of fMRI processing steps.
The resting-state fMRI raw data from 46 same-sex twin pairs were pre-processed using SPM12. As follow, the data denoising of the pre-processed fMRI data was applied by the use of a semiautomatic tool for artefacts identification and subsequently the artefactual independent components (ICs) were regressed out from the data. At this step, the subjects associated with a percentage of noise-related ICs > 75% and their siblings were excluded from the analyses, resulting in a dataset composed of 43 twin pairs. Seed-based functional connectivity (FC)

Node-level FC metrics Description
Local efficiency It describes the regional efficiency indicating the integration of each node. It represents the global efficiency computed on the neighbourhood of the node (extracted from binarized FC matrix). Clustering coefficient It describes the ability for functional segregation and efficiency of local information transfer (extracted from binarized FC matrix). Degree It represents number of links connected to the node indicating how many connections a node has to other nodes (extracted from binarized FC matrix).

Strength of weights
It indicates the average weight of connections each node has with other nodes. Calculated as the sum of the nodes associated to positive and negative weights (extracted from weighted FC matrix).
The strength of positive weight is calculated as the sum of the links connected to the nodes associated to a positive correlation with the others and represents the total positive strength of the node, and how much this node communicates in synchrony with the others.
The strength of negative weight is calculated as the sum of the links connected to the nodes associated to a negative correlation with the others and represents the total negative strength of the node, and how much this node communicates in asynchrony with the others.

Betweenees centrality
It indicates the fraction of all shortest paths in the network that contain a given node, if a node has a highest fraction of shortest paths, more communication across the network will pass through this node (extracted from binarized FC matrix).

Global FC metrics Description
Global efficiency It is estimated as the average inverse shortest path length in the network, representing the integration over the whole brain network (extracted from binarized FC matrix). Characteristic path length It is calculated as the average shortest path length in the network and representing how "efficiently" the brain is connected (extracted from binarized FC matrix). Degree It represents the average degree in the network (see Table S1, extracted from binarized FC matrix). Density It indicates the fraction of present connections out of all possible connections, indicate how densely connected is a graph (extracted from binarized FC matrix). Louvain modularity It represents the modularity of a network and quantifies the "strength" of partition of a network into modules (also called communities and clusters). Louvain method is an efficient method to identify communities in large network. The method is a greedy optimization that attempts to optimize the modularity of a partition of a network by: firstly, it looks for small communities optimizing modularity locally, then it aggregates nodes belonging to the same community and builds a new network whose nodes are the communities (extracted from binarized FC matrix).